03903nam 22006975 450 991064589310332120240419053529.0981-19-8937-010.1007/978-981-19-8937-7(MiAaPQ)EBC7184195(Au-PeEL)EBL7184195(CKB)26027661600041(DE-He213)978-981-19-8937-7(PPN)267808534(EXLCZ)992602766160004120230118d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDeep Learning in Cancer Diagnostics A Feature-based Transfer Learning Evaluation /by Mohd Hafiz Arzmi, Anwar P. P. Abdul Majeed, Rabiu Muazu Musa, Mohd Azraai Mohd Razman, Hong-Seng Gan, Ismail Mohd Khairuddin, Ahmad Fakhri Ab. Nasir1st ed. 2023.Singapore :Springer Nature Singapore :Imprint: Springer,2023.1 online resource (41 pages)SpringerBriefs in Forensic and Medical Bioinformatics,2196-8853Print version: Arzmi, Mohd Hafiz Deep Learning in Cancer Diagnostics Singapore : Springer,c2023 9789811989360 1. Epidemiology, detection and management of cancer -- 2. A VGG16 feature-based Transfer Learning Evaluation for the diagnosis of Oral Squamous Cell Carcinoma (OSCC) -- 3. The Classification of Breast Cancer: The effect of hyperparameter optimisation towards the efficacy of feature-based transfer learning pipeline -- 4. The Classification of Lung Cancer: A DenseNet feature-based Transfer Learning Evaluation -- 5. Skin Cancer Diagnostics: A VGG Ensemble Approach -- 6. The Way Forward.Cancer is the leading cause of mortality in most, if not all, countries around the globe. It is worth noting that the World Health Organisation (WHO) in 2019 estimated that cancer is the primary or secondary leading cause of death in 112 of 183 countries for individuals less than 70 years old, which is alarming. In addition, cancer affects socioeconomic development as well. The diagnostics of cancer are often carried out by medical experts through medical imaging; nevertheless, it is not without misdiagnosis owing to a myriad of reasons. With the advancement of technology and computing power, the use of state-of-the-art computational methods for the accurate diagnosis of cancer is no longer far-fetched. In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer.SpringerBriefs in Forensic and Medical Bioinformatics,2196-8853Medical physicsArtificial intelligenceCancerImagingComputational intelligenceMedical PhysicsArtificial IntelligenceCancer ImagingComputational IntelligenceMedical physics.Artificial intelligence.CancerImaging.Computational intelligence.Medical Physics.Artificial Intelligence.Cancer Imaging.Computational Intelligence.610.153Arzmi Mohd Hafiz1275821Abdul Majeed Anwar. P. PMuazu Musa RabiuMohd Razman Mohd AzraaiGan Hong-SengMohd Khairuddin IsmailAb. Nasir Ahmad FakhriMiAaPQMiAaPQMiAaPQBOOK9910645893103321Deep Learning in Cancer Diagnostics4154887UNINA03747nam 2201057z- 450 991055741400332120210501(CKB)5400000000043555(oapen)https://directory.doabooks.org/handle/20.500.12854/68513(oapen)doab68513(EXLCZ)99540000000004355520202105d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierFrom Basic Research to New Tools and Challenges for the Genotoxicity Testing of NanomaterialsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (188 p.)3-0365-0112-6 3-0365-0113-4 This Special Issue presents studies on the genotoxicity of nanomaterials. Although nanomaterials provide multiple benefits in a wide range of applications, challenges remain in addressing strong concerns about their risks to the environment and human health. As a result of inconsistencies among published results and diverging conclusions, the understanding of nanomaterial exposure and toxicity remains unclear. Determining whether these materials cause DNA damage-the first step in carcinogenesis-must be a priority in testing. In this book, readers will find recent publications on the genotoxic response to a broad range of nanomaterials, the impact of physico-chemical characteristics, safe-by-design and new developed tools.HumanitiesbicsscSocial interactionbicssc3D cultureadvanced in vitro modelAgNPAllium cepaaluminumantioxidant activityapoptosisBEAS-2B cells.CeO2NPcomet assaycytotoxiccytotoxicityDNA damageDNA methylationepigeneticsFPG enzymegenotoxicgenotoxicitygraphene oxideguthepatotoxicityHepG2high throughput screeningHprtHs27 human fibroblastshuman amniotic cellsin vitro genotoxicityin vitro testinglincomycinliverliver spheroidsmetal oxidesmetal/coating agent ratiomicronuclei formationmicronucleusmicronucleus assaymulti-walled carbon nanotubes (MWCNT)n/ananomaterialnanoparticlesnanoplasticsnanotoxicologynongenotoxic silver nanoparticlesoral routeoxidative stresspolystyrene nanoparticlesreduced graphene oxidesafer-by-designsilver ionsSiO2NPTiO2NPtitanium dioxide nanoparticlestritiated particlestungstenV79 cellsZnONPHumanitiesSocial interactionFESSARD Valérieedt1297531Nesslany FabriceedtFESSARD ValérieothNesslany FabriceothBOOK9910557414003321From Basic Research to New Tools and Challenges for the Genotoxicity Testing of Nanomaterials3024504UNINA